The document discusses building smarter applications using model-based approaches rather than rule-based or rigid hard-coded architectures. It describes using H2O to combine applications with machine learning models to enable the applications to evolve and learn from data. The document provides an example of using H2O and data on US club loans from 2007-2015 to build an application that can predict whether a customer would be eligible for a loan and the interest rate.

1. Building Smarter Application

2. What is a smart application?
Rule-based
Riged
Hard coded
Model-based
Evolves
Learning process

3. Objective
Combine Application with models

4. Architecture

5. How it works

7. Installation
Current environment
○ Ubuntu 16.04
○ Java 8
○ R
Terminal commands
$ cd ~/Downloads
$ unzip h2o-3.10.4.6.zip
$ cd h2o-3.10.4.6
$ java -jar h2o.jar

8. Implementation
H2O offers two ways for data prediction:
1. Using Code such as R, Java, Python, Coffeescript/Javascript, and Scala
2. Using the web interface Flow.

9. Question
What answer we’re looking for?
Would this client be
eligible for a loan and how
much interest would one
pay?

10. Data
● Club loans dates from 2007 to 2015 in United States
● The data used in the implementation is clean
● 163,987 Frames (instance or row)
● 15 Features (column)

11. Data Description in Flow

12. Simple Code in R

13. Loan Customer Implementation
$ gradle wrapper
$ ./gradlew build
$ ./gradlew jettyRunWar -x generateModels
Visit http://localhost:8080